6 citations · 12 across the 11 of their papers we have counts for
12 papers
Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM
Peiyang Xu, Xiaopei Zhu, Jun Zhu +1
Existing research on object hallucination in multimodal large language models (MLLMs) predominantly attributes the problem to language priors such as over-reliance on textual co-oc…
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
Xiaopei Zhu, Siyuan Huang, Zhanhao Hu +3
Thermal Infrared detection is widely used in autonomous driving, medical AI, etc., but its security has only attracted attention recently. We propose infrared adversarial clothing…
Mirror Illusion Art
Xiaopei Zhu, Zeyuan Li, Jun Zhu +1
Mirror Illusion Art is a novel reflection-conditioned 3D illusion where one object yields two target appearances (front and mirror). The task is formulated as inverse design from t…
Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern
Xiaopei Zhu, Guanning Zeng, Zhanhao Hu +2
Visible-thermal (RGB-T) object detection is a crucial technology for applications such as autonomous driving, where multimodal fusion enhances performance in challenging conditions…
A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
Wei Zhang, Zhanhao Hu, Xiao Li +2
In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers…
Crafting Physical Adversarial Examples by Combining Differentiable and Physically Based Renders
Yuqiu Liu, Huanqian Yan, Xiaopei Zhu +4
Recently we have witnessed progress in hiding road vehicles against object detectors through adversarial camouflage in the digital world. The extension of this technique to the phy…
Natural Language Induced Adversarial Images
Xiaopei Zhu, Peiyang Xu, Guanning Zeng +2
Research of adversarial attacks is important for AI security because it shows the vulnerability of deep learning models and helps to build more robust models. Adversarial attacks o…
Infrared Adversarial Car Stickers
Xiaopei Zhu, Yuqiu Liu, Zhanhao Hu +2
Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Prev…
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
Zhanhao Hu, Wenda Chu, Xiaopei Zhu +3
Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans.…
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World
Xiaopei Zhu, Zhanhao Hu, Siyuan Huang +2
Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We propos…
Adversarial Texture for Fooling Person Detectors in the Physical World
Zhanhao Hu, Siyuan Huang, Xiaopei Zhu +3
Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately desig…
Fooling thermal infrared pedestrian detectors in real world using small bulbs
Xiaopei Zhu, Xiao Li, Jianmin Li +2
Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. They have the unique advantages…